gNMI and Databricks Integration
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Powerful Performance, Limitless Scale
Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.
See Ways to Get Started
Input and output integration overview
The gNMI (gRPC Network Management Interface) Input Plugin collects telemetry data from network devices using the gNMI Subscribe method. It supports TLS for secure authentication and data transmission.
Use Telegraf’s HTTP output plugin to push metrics straight into a Databricks Lakehouse by calling the SQL Statement Execution API with a JSON-wrapped INSERT or volume PUT command.
Integration details
gNMI
This input plugin is vendor-agnostic and can be used with any platform that supports the gNMI specification. It consumes telemetry data based on the gNMI Subscribe method, allowing for real-time monitoring of network devices.
Databricks
This configuration turns Telegraf into a lightweight ingestion agent for the Databricks Lakehouse. It leverages the Databricks SQL Statement Execution API 2.0, which accepts authenticated POST requests containing a JSON payload with a statement
field. Each Telegraf flush dynamically renders a SQL INSERT (or, for file-based workflows, a PUT ... INTO /Volumes/...
command) that lands the metrics into a Unity Catalog table or volume governed by Lakehouse security. Under the hood Databricks stores successful inserts as Delta Lake transactions, enabling ACID guarantees, time-travel, and scalable analytics. Operators can point the warehouse_id
at any serverless or classic SQL warehouse, and all authentication is handled with a PAT or service-principal token—no agents or JDBC drivers required. Because Telegraf’s HTTP output supports custom headers, batching, TLS, and proxy settings, the same pattern scales from edge IoT gateways to container sidecars, consolidating infrastructure telemetry, application logs, or business KPIs directly into the Lakehouse for BI, ML, and Lakehouse Monitoring. Unity Catalog volumes provide a governed staging layer when file uploads and COPY INTO
are preferred, and the approach aligns with Databricks’ recommended ingestion practices for partners and ISVs.
Configuration
gNMI
[[inputs.gnmi]]
## Address and port of the gNMI GRPC server
addresses = ["10.49.234.114:57777"]
## define credentials
username = "cisco"
password = "cisco"
## gNMI encoding requested (one of: "proto", "json", "json_ietf", "bytes")
# encoding = "proto"
## redial in case of failures after
# redial = "10s"
## gRPC Keepalive settings
## See https://pkg.go.dev/google.golang.org/grpc/keepalive
## The client will ping the server to see if the transport is still alive if it has
## not see any activity for the given time.
## If not set, none of the keep-alive setting (including those below) will be applied.
## If set and set below 10 seconds, the gRPC library will apply a minimum value of 10s will be used instead.
# keepalive_time = ""
## Timeout for seeing any activity after the keep-alive probe was
## sent. If no activity is seen the connection is closed.
# keepalive_timeout = ""
## gRPC Maximum Message Size
# max_msg_size = "4MB"
## Enable to get the canonical path as field-name
# canonical_field_names = false
## Remove leading slashes and dots in field-name
# trim_field_names = false
## Guess the path-tag if an update does not contain a prefix-path
## Supported values are
## none -- do not add a 'path' tag
## common path -- use the common path elements of all fields in an update
## subscription -- use the subscription path
# path_guessing_strategy = "none"
## Prefix tags from path keys with the path element
# prefix_tag_key_with_path = false
## Optional client-side TLS to authenticate the device
## Set to true/false to enforce TLS being enabled/disabled. If not set,
## enable TLS only if any of the other options are specified.
# tls_enable =
## Trusted root certificates for server
# tls_ca = "/path/to/cafile"
## Used for TLS client certificate authentication
# tls_cert = "/path/to/certfile"
## Used for TLS client certificate authentication
# tls_key = "/path/to/keyfile"
## Password for the key file if it is encrypted
# tls_key_pwd = ""
## Send the specified TLS server name via SNI
# tls_server_name = "kubernetes.example.com"
## Minimal TLS version to accept by the client
# tls_min_version = "TLS12"
## List of ciphers to accept, by default all secure ciphers will be accepted
## See https://pkg.go.dev/crypto/tls#pkg-constants for supported values.
## Use "all", "secure" and "insecure" to add all support ciphers, secure
## suites or insecure suites respectively.
# tls_cipher_suites = ["secure"]
## Renegotiation method, "never", "once" or "freely"
# tls_renegotiation_method = "never"
## Use TLS but skip chain & host verification
# insecure_skip_verify = false
## gNMI subscription prefix (optional, can usually be left empty)
## See: https://github.com/openconfig/reference/blob/master/rpc/gnmi/gnmi-specification.md#222-paths
# origin = ""
# prefix = ""
# target = ""
## Vendor specific options
## This defines what vendor specific options to load.
## * Juniper Header Extension (juniper_header): some sensors are directly managed by
## Linecard, which adds the Juniper GNMI Header Extension. Enabling this
## allows the decoding of the Extension header if present. Currently this knob
## adds component, component_id & sub_component_id as additional tags
# vendor_specific = []
## YANG model paths for decoding IETF JSON payloads
## Model files are loaded recursively from the given directories. Disabled if
## no models are specified.
# yang_model_paths = []
## Define additional aliases to map encoding paths to measurement names
# [inputs.gnmi.aliases]
# ifcounters = "openconfig:/interfaces/interface/state/counters"
[[inputs.gnmi.subscription]]
## Name of the measurement that will be emitted
name = "ifcounters"
## Origin and path of the subscription
## See: https://github.com/openconfig/reference/blob/master/rpc/gnmi/gnmi-specification.md#222-paths
##
## origin usually refers to a (YANG) data model implemented by the device
## and path to a specific substructure inside it that should be subscribed
## to (similar to an XPath). YANG models can be found e.g. here:
## https://github.com/YangModels/yang/tree/master/vendor/cisco/xr
origin = "openconfig-interfaces"
path = "/interfaces/interface/state/counters"
## Subscription mode ("target_defined", "sample", "on_change") and interval
subscription_mode = "sample"
sample_interval = "10s"
## Suppress redundant transmissions when measured values are unchanged
# suppress_redundant = false
## If suppression is enabled, send updates at least every X seconds anyway
# heartbeat_interval = "60s"
Databricks
[[outputs.http]]
## Databricks SQL Statement Execution API endpoint
url = "https://{{ env "DATABRICKS_HOST" }}/api/2.0/sql/statements"
## Use POST to submit each Telegraf batch as a SQL request
method = "POST"
## Personal-access token (PAT) for workspace or service principal
headers = { Authorization = "Bearer {{ env "DATABRICKS_TOKEN" }}" }
## Send JSON that wraps the metrics batch in a SQL INSERT (or PUT into a Volume)
content_type = "application/json"
## Serialize metrics as JSON so they can be embedded in the SQL statement
data_format = "json"
json_timestamp_units = "1ms"
## Build the request body. Telegraf replaces the template variables at runtime.
## Example inserts a row per metric into a Unity-Catalog table.
body_template = """
{
\"statement\": \"INSERT INTO ${TARGET_TABLE} VALUES {{range .Metrics}}(from_unixtime({{.timestamp}}/1000), {{.fields.usage}}, '{{.tags.host}}'){{end}}\",
\"warehouse_id\": \"${WAREHOUSE_ID}\"
}
"""
## Optional: add batching limits or TLS settings
# batch_size = 500
# timeout = "10s"
Input and output integration examples
gNMI
-
Monitoring Cisco Devices: Use the gNMI plugin to collect telemetry data from Cisco IOS XR, NX-OS, or IOS XE devices for performance monitoring.
-
Real-time Network Insights: With the gNMI plugin, network administrators can gain insights into real-time metrics such as interface statistics and CPU usage.
-
Secure Data Collection: Configure the gNMI plugin with TLS settings to ensure secure communication while collecting sensitive telemetry data from devices.
-
Flexible Data Handling: Use the subscription options to customize which telemetry data you want to collect based on specific needs or requirements.
-
Error Handling: The plugin includes troubleshooting options to handle common issues like missing metric names or TLS handshake failures.
Databricks
- Edge-to-Lakehouse Telemetry Pipe: Deploy Telegraf on factory PLCs to sample vibration metrics and post them every second to a serverless SQL warehouse. Delta tables power PowerBI dashboards that alert engineers when thresholds drift.
- Blue-Green CI/CD Rollout Metrics: Attach a Telegraf sidecar to each Kubernetes canary pod; it inserts container stats into a Unity Catalog table tagged by
deployment_id
, letting Databricks SQL compare error-rate percentiles and auto-rollback underperforming versions. - SaaS Usage Metering: Insert per-tenant API-call counters via the HTTP plugin; a nightly Lakehouse query aggregates usage into invoices, eliminating custom metering micro-services.
- Security Forensics Lake: Upload JSON batches of Suricata IDS events to a Unity Catalog volume using
PUT
commands, then runCOPY INTO
for near-real-time enrichment with Delta Live Tables, producing a searchable threat-intel lake that joins network logs with user session data.
Feedback
Thank you for being part of our community! If you have any general feedback or found any bugs on these pages, we welcome and encourage your input. Please submit your feedback in the InfluxDB community Slack.
Powerful Performance, Limitless Scale
Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.
See Ways to Get Started
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